Microalgae are recognized as sustainable biofactories for metabolites relevant to nutraceutical, pharmaceutical, and marine drug applications. Nannochloropsis salina is notable for its production of bioactive lipids, including the pharmaceutically relevant omega-3 eicosapentaenoic acid (EPA). Here, we evaluated orange peel extract (OPE), a citrus by-product, as a substrate for mixotrophic cultivation of N. salina within a bioeconomy framework. OPE supplementation triggered dose-dependent physiological responses. Among the tested OPE concentrations, 5% supplementation resulted in the highest total fatty acid content, increasing fatty acids from 24.4% (control) to 30.6% and EPA from 6.4% to 9.8%, whereas 10% OPE maintained biomass pigmentation. Higher OPE levels enhanced antioxidant potential, raising total antioxidant capacity from 4.6 (control) to 6.7 mg/g vitamin C equivalents (with 10% OPE), while the highest concentrations induced metabolic stress, reducing biomass and altering lipid composition. Fourier transform infrared spectroscopy analyses revealed biochemical adjustments consistent with metabolic reorganization, including increased intensities of the amide I and II bands associated with protein-rich structures. Overall, OPE emerges as a cost-effective supplement capable of modulating the biochemical quality of N. salina while valorizing agro-industrial residues. The increases in EPA and antioxidant capacity support the potential of OPE-supplemented cultures as a platform for the production of marine-derived bioactive compounds.
Carlo Esposito, G. Aldini, Yanan Yin et al.· BioTech· 0 citations
Bioelectrochemical systems (BES) represent an interdisciplinary convergence of biology, electrochemistry, materials science, environmental engineering and mechanical engineering, offering transformative potential for renewable energy generation, wastewater treatment and resource valorization. However, the inherent structural intricacy and mechanistic complexity of BES pose significant challenges to system understanding and optimization. With robust capabilities in pattern recognition and nonlinear system modeling, machine learning (ML) appears to be a good approach to decipher the complex mechanisms of BES. A systematic literature review reveals that ML applications in BES date back to 2006, with a marked surge around 2021, reflecting the growing research interest in this interdisciplinary field. The application domains primarily fall into four categories: (1) analysis and prediction of microbial communities, (2) intelligent design of system components, (3) performance prediction and system optimization, and (4) real-time monitoring and assisted intelligent control. Among these, performance prediction and system optimization constitute the dominant application area, and model interpretability and generalization are cross-cutting requirements for reliable and transferable ML deployment in BES. Among the BES subtypes that have employed ML, microbial fuel cells (MFC) account for the largest share (42.4%), followed by electrochemical biosensor (EB, 37.6%) and microbial electrolysis cells (MEC, 10.9%), with other types occupying smaller proportions. Regarding algorithmic choices, artificial neural networks are the most frequently used method (30.9%), followed by support vector machines or support vector regression (17.3%), principal component analysis (14.5%), and regression trees (13.1%). ML has exhibited significant potential in elevating BES design, manufacture, operation and application. However, constrained by data scarcity and heterogeneity, present models are with limited transferability across scales. Further efforts are warranted to promote the application of ML in BES by expanding data accumulation, diversifying datasets, and developing targeted models. This will ultimately enable a deeper understanding, enhanced optimization and broader deployment of BES.